Knowledge Graphs for Textbooks: Extraction and Completion Techniques
Publication Date
1-1-2023
Document Type
Conference Proceeding
Publication Title
Proceedings - 2023 IEEE 6th International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023
DOI
10.1109/AIKE59827.2023.00014
First Page
38
Last Page
45
Abstract
This paper aims to apply knowledge graph construction techniques to textbooks, explicitly focusing on the challenge of the absence of domain-specific schema for each textbook. Various entity and relation extraction models are utilized to capture logical and semantic information related to the textbook s topic. These models include a Text-Encoding-Initiative (TEI) model to extract hierarchical concepts, spaCy Natural Language Processing (NLP), and Google Cloud Natural Language to extract semantic information from the main textual content. The study includes a case study on a cloud computing textbook, where each approach is evaluated and analyzed. Ultimately, the goal is to create knowledge graphs of textbooks, enabling the completion task of predicting missing entities or relations in a low-dimensional space.
Funding Number
22-RSG-08-034
Keywords
Domain-enrichment, Entity recognition, Knowledge Graphs, Natural Language Processing, Relation extraction, Textbook analysis
Department
Computer Science
Recommended Citation
Yutong Yao, Petros Potikas, and Katerina Potika. "Knowledge Graphs for Textbooks: Extraction and Completion Techniques" Proceedings - 2023 IEEE 6th International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023 (2023): 38-45. https://doi.org/10.1109/AIKE59827.2023.00014
